Provincial profile: Gauteng - Statistics South Africa ... · Statistics South Africa Census 2011...

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Provincial profile: Gauteng The South Africa I know, the home I understand

Transcript of Provincial profile: Gauteng - Statistics South Africa ... · Statistics South Africa Census 2011...

Provincial profile: Gauteng

The South Africa I know, the home I understand

Census 2011 Provincial Profile: Gauteng, Report 03-01-76

Provincial profile: Gauteng

Census 2011

Statistics South Africa

Pali LehohlaStatistician-GeneralReport No. 03-01-76 (2011)

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Census 2011 Provincial Profile: Gauteng / Statistics South Africa Published by Statistics South Africa, Private Bag X44, Pretoria 0001 © Statistics South Africa, 2014 Users may apply or process this data, provided Statistics South Africa (Stats SA) is acknowledged as the original source of the data; that it is specified that the application and/or analysis is the result of the user's independent processing of the data; and that neither the basic data nor any reprocessed version or application thereof may be sold or offered for sale in any form whatsoever without prior permission from Stats SA. Stats SA Library Cataloguing-in-Publication (CIP) Data Census 2011 Provincial Profile: Gauteng / Statistics South Africa. Pretoria: Statistics South Africa 2014 58p. [Report No. 03-01-76 (2011)] ISBN: 978-0-621-43215-2 A complete set of Stats SA publications is available at the Stats SA Library and the following libraries: National Library of South Africa, Pretoria Division National Library of South Africa, Cape Town Division Natal Society Library, Pietermaritzburg Library of Parliament, Cape Town Bloemfontein Public Library Johannesburg Public Library Eastern Cape Library Services, King William's Town Central Regional Library, Polokwane Central Reference Library, Nelspruit Central Reference Collection, Kimberley Central Reference Library, Mmabatho This publication is available on the Stats SA website: www.statssa.gov.za Enquiries: Gauteng Provincial Office Tel: (011) 833 0100

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Table of contents Chapter 1: Introduction ................................................................................................................... 1

1.1 Overview............................................................................................................................................. 1

1.2 How the count was done.................................................................................................................... 1 1.2.1 Planning ...................................................................................................................................... 1

1.2.2 Pre-enumeration ........................................................................................................................ 1

1.2.3 Enumeration ............................................................................................................................... 2 1.2.4 Data processing .......................................................................................................................... 2

1.2.5 Data editing and validation system ............................................................................................ 2

1.2.6 Independent monitoring and evaluation of census field activities ............................................ 4

1.2.7 Post-enumeration survey (PES) .................................................................................................. 4 1.3 Conclusion ........................................................................................................................................10

Chapter 2: Geography of South Africa ........................................................................................... 11

2.1 Provincial boundary changes, 2001–2011 .......................................................................................11 2.2 Local municipal boundary changes, 2001–2011 ..............................................................................14

Chapter 3: Results pertaining to persons ....................................................................................... 16

3.1 Introduction ......................................................................................................................................16 3.2 Population size and distribution .......................................................................................................16

3.3 Languages .........................................................................................................................................20

3.4 Migration ..........................................................................................................................................21

3.5 General health and functioning........................................................................................................24 3.6 Education ..........................................................................................................................................25

3.7 Employment .....................................................................................................................................28

Chapter 4: Results pertaining to households .................................................................................. 32 4.1 Introduction ......................................................................................................................................32

4.2 Households .......................................................................................................................................32

Chapter 5: Discussion and conclusion ............................................................................................ 41 5.1 Background .......................................................................................................................................41

5.2 The population of Gauteng ..............................................................................................................41

5.3 Migration ..........................................................................................................................................41

5.4 Education ..........................................................................................................................................41 5.5 The labour market ............................................................................................................................41

5.6 Households and household services ................................................................................................42

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List of tables Table 2.1: Geographical land area changes since 2001 .........................................................................11

Table 3.1: Population and percentage share by province, Censuses 1996, 2001 and 2011 ..................16

Table 3.2: Population size by province and population group, 2011 .....................................................16 Table 3.3: Population size and percentage share by district municipalities, Censuses 1996, 2001 and 2011 .................................................................................................................................................17 Table 3.4: Population size by district municipalities and population group, 2011 ................................18

Table 3.5: Percentages and dependency ratios .....................................................................................19

Table 3.6: Population distribution of each official language most often spoken at home within each district municipalities .....................................................................................................................20

Table 3.7: Distribution of population aged 5 years and older by disability status, sex, numbers and percentages .....................................................................................................................................24

Table 4.1: Distribution of households by district/local municipalities, Censuses 1996, 2001 and 2011 ........................................................................................................................................................32

Table 4.2: Headship by district/local municipalities ...............................................................................33

List of figures Figure 2.1: Percentage distribution of land area by province, 2011 ......................................................15

Figure 3.1: Percentage distribution of the population groups in Gauteng, Censuses 1996, 2001 and 2011 .................................................................................................................................................17

Figure 3.2: Distribution of the Gauteng population by age group, 2011 ...............................................18 Figure 3.3: Dependency ratio by district municipalities, Censuses 1996, 2001 and 2011 .....................19

Figure 3.4: Percentage distribution of migrants in Gauteng, 2011 ........................................................21

Figure 3.5: Net loss or gain of people in each province through inter-provincial migration, South Africa, 2001 and 2011 ............................................................................................................................21

Figure 3.6: Percentage distribution of internal migrants by 5-year age group ......................................22

Figure 3.7: Net loss or gain by gender in each province through inter-provincial migration, Census 2011 ...........................................................................................................................................22

Figure 3.8: Net migration by gender for the age group 0–14 ................................................................23 Figure 3.9: Net migration by gender for the age group 15–64 ..............................................................23

Figure 3.10: Net migration by gender for the age group 65+ ................................................................24

Figure 3.11: Percentage of persons aged 5–24 who were attending an educational institution in Gauteng, Censuses 2001 and 2011 ........................................................................................................25

Figure 3.12: Attendance at an educational institution by gender in Gauteng, Census 2001 ................26

Figure 3.13: Attendance at an educational institution by gender in Gauteng, Census 2011 ................26 Figure 3.14: Percentage of persons aged 5–24 years currently attending an educational institution by type of institution, Censuses 2001 and 2011 ...................................................................27

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Figure 3.15: Highest level of education attained by persons aged 20 years and older by gender, Censuses 2001 and 2011 ........................................................................................................................27

Figure 3.16: Highest level of education attained by persons aged 20 years and older by population group, Censuses 2001 and 2011 ..........................................................................................28

Figure 3.17: Unemployment rate by population group, Census 2011 ...................................................29 Figure 3.18: Unemployment rate (official) by sex and age group ..........................................................29

Figure 3.19: Unemployment rate (expanded) by sex and age group, Gauteng, Census 2011 ...............30

Figure 3.20: Unemployment rate (official and expanded) by sex and age group in Gauteng, Census 2011 ...........................................................................................................................................30

Figure 3.21: Unemployment rate (official and expanded) by sex and age group in Gauteng, Census 2011 ...........................................................................................................................................31

Figure 4.1: Average households size by municipalities, Censuses 1996, 2001 and 2011 ......................33

Figure 4.2: Percentage distribution of households by type of main dwelling, Censuses 1996, 2001 and 2011 .................................................................................................................................................34

Figure 4.3: Percentage distribution of households by tenure status, Censuses 2001 and 2011 ...........35 Figure 4.4: Percentage distribution of households by mode of refuse removal by district municipalities, Censuses 1996, 2001 and 2011 ......................................................................................36

Figure 4.5: Percentage distribution of households by type of toilet facility, Censuses 1996, 2001 and 2011 .................................................................................................................................................36

Figure 4.6: Percentage distribution of households by type of energy used for cooking by district municipalities, Censuses 1996, 2001 and 2011 ......................................................................................37

Figure 4.7: Percentage distribution of households by type of energy used for lighting, Censuses 1996, 2001 and 2011 ..............................................................................................................................38

Figure 4.8: Percentage distribution of households by type of energy used for heating, Censuses 1996, 2001 and 2011 ..............................................................................................................................39

Figure 4.9: Percentage of households that have access to piped (tap) water, Censuses 2001 and 2011 ........................................................................................................................................................40

Figure 4.10: Percentage of households with household goods in working order, Censuses 2001 and 2011 .................................................................................................................................................40

List of maps Map 2.1: Provincial boundary changes since 2001 ................................................................................12

Map 2.2: Municipal boundary changes since 2001 ................................................................................14

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Annexure Tables Table 1: Percentage distribution of total population by five-year age groups and sex, Gauteng, Census 2011 ...........................................................................................................................................43

Table 2: Population distribution by official language most often spoken at home within each district municipality, Census 2011 ..........................................................................................................47

Table 3: Persons aged 5–24 years currently attending an educational institution by type of institution, Census 2011 .........................................................................................................................48

Table 4: Highest level of education attained by persons aged 20 years and older, Census 2011 .........49

Table 5: Percentage distribution of population aged 5 years and older by degree of difficulty (seeing) ...................................................................................................................................................50

Table 6: Percentage distribution of population aged 5 years and older by degree of difficulty (hearing) .................................................................................................................................................50

Table 7: Percentage distribution of population aged 5 years and older by degree of difficulty (communication) ....................................................................................................................................51

Table 8: Percentage distribution of population aged 5 years and older by degree of difficulty (walking or climbing stairs) ....................................................................................................................51

Table 9: Percentage distribution of population aged 5 years and older by degree of difficulty (remembering or concentration) ...........................................................................................................52

Table 10: Percentage distribution of population aged 5 years and older by degree of difficulty (self-care) ................................................................................................................................................52

Figures Figure 1: Distribution of total population by five-year age groups and sex, South Africa, Census 2011 ........................................................................................................................................................43

Figure 2: Distribution of total population by five-year age groups and sex, Johannesburg, Census 2011 ........................................................................................................................................................44

Figure 3: Distribution of total population by five-year age groups and sex, Ekurhuleni, Census 2011 ........................................................................................................................................................44

Figure 4: Distribution of total population by five-year age groups and sex, City of Tshwane, Census 2011 ...........................................................................................................................................45

Figure 5: Distribution of total population by five-year age groups and sex, Sedibeng, Census 2011 ....45

Figure 6: Distribution of total population by five-year age groups and sex, West Rand, Census 2011 ........................................................................................................................................................46

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Chapter 1: Introduction

1.1 Overview

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation. South Africa has conducted three censuses (1996, 2001 and 2011). Census 2011 was the third census to be conducted since the post-democratic elections in 1994 and a number of population and household attributes were measured and a variety of indicators generated. This chapter provides profiles results on all census topics: demographics, migration, education, general health and functioning, labour force, mortality, and households.

1.2 How the count was done

Census 2011 was conducted from 9–31 October 2011. This section focuses on the various activities that were carried out prior to the finalisation of the results. They can be summarised as follows: planning, pre-enumeration, enumeration, processing and editing.

1.2.1 Planning

This process involved the development of the overall strategy, the structure for the project, component plans and budget. These processes were started in 2003 and were subsequently reviewed in 2008, after the completion of the Community Survey (CS) in 2007. Methodologies and procedures were then developed and tested in a form of mini tests and a pilot in 2008 and 2009 respectively. The findings from these tests helped to refine the plans and methods for the final test in 2010 called the “Dress Rehearsal”. The latter was expected to be a replica of how the actual count was to be conducted in 2011, and therefore the timing had to be the same month as the main census, i.e. October.

1.2.2 Pre-enumeration

The pre-enumeration phase mainly involved the final preparatory work before the actual count. It started with mass production of census instruments like questionnaires, manuals, field gear etc. The phase also involved acquisition of satellite offices required in the districts, recruitment of the first level of field management staff (District Census Coordinators – 130 DCCs) and Fieldwork Coordinators (6 000 FWCs). These groups of people were then given intense training based on their key performance areas. At the same time the country was being sub-divided into small pockets called enumeration areas (EAs); the underlying principle for this sub-division is that an EA should be within reach of a Fieldworker and all households in that EA can be covered within the allocated number of days. This process yielded 103 576 EAs. The other benefit for this sub-division is the finalisation of the distribution plan of all materials required in the provinces and districts. It also gives a better estimate of the

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number of field staff to recruit for the count. The pre-enumeration phase involved over 7 000 staff.

1.2.3 Enumeration

The enumeration phase started with the training of supervisors as listers. Each person had to list all dwellings within an EA and had a minimum of four EAs to cover. These areas were called supervisory units. As they were listing, they were also expected to publicise the activities of the census within their supervisory units. Upon completion of listing, final adjustments of workload and number of enumerators required were finalised. Training of enumerators started in earnest, and it mainly covered how to complete the questionnaire and to read a map. The latter was to aid them to identify the boundaries of their assigned areas. An enumerator was also given a few days before the start of the count to update their orientation book with any developments that might have happened since listing, as well as introduce themselves to the communities they were to work with, through posters bearing their photos and special identification cards. On the night of 9 October 2011 the actual count started with the homeless and special institutions given special attention. The enumeration phase was undertaken by an army of field staff in excess of 160 000, inclusive of management.

1.2.4 Data processing

The processing of over 15 million questionnaires commenced in January 2012, immediately after the completion of the reverse logistics in December 2011. Each box and its contents were assigned a store location in the processing centre via a store management system. Each time a box was required for any process it was called through this system. The processing phase was sub-divided in the following processes: primary preparation – where all completed questionnaires were grouped into clusters of 25 and the spine of the questionnaire was cut off; secondary preparation – where questionnaires were finally prepared for scanning by removing foreign materials in between pages and ensure that all pages are loose; scanning – questionnaires were put through a scanner to create an electronic image; and finally tilling and completion – where any unrecognised reading/badly-read image by the scanner had to be verified by a data capturer. This process took eight months. Over 2 000 data processors working three shifts per day were employed for this phase, to ensure that 225 million single pages are accounted for.

1.2.5 Data editing and validation system

The execution of each phase of census operations introduces some form of errors in census data. Despite quality assurance methodologies embedded in all the phases; data collection, data capturing (both manual and automated), coding, and editing, a number of errors creep in and distort the collected information. To promote consistency and improve on data quality, editing is a paramount phase in identifying and minimising errors such as invalid

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values, inconsistent entries or unknown/missing values. The editing process for Census 2011 was based on defined rules (specifications).

The editing of Census 2011 data involved a number of sequential processes: selection of members of the editing team, review of Census 2001 and 2007 Community Survey editing specifications, development of editing specifications for the Census 2011 pre-tests (2009 pilot and 2010 Dress Rehearsal), development of firewall editing specifications and finalisation of specifications for the main census.

Editing team

The Census 2011 editing team was drawn from various divisions of the organisation based on skills and experience in data editing. The team was thus composed of subject matter specialists (demographers and programmers), managers as well as data processors.

Role of the team

Among other census activities, the editing team’s roles and responsibilities included:

• Establishment of editing plan/schedule • Formulation and application of clear and concise editing specifications • Validation of census data using other data sources • Ensure consistency of editing rules between censuses (2001 and 2011) where

applicable • Provision of imputation flags and rates • Identification of errors and provide corrections where possible • Review and refinement of the editing specifications based on edit trail evaluations,

cross tabulations, and comparison of census data with other datasets • Testing the specifications before confirming and applying them

The editing specification process commenced with activities relating to review of existing editing specifications guidelines. Census 2001 specifications as well as Community Survey 2007 survey specifications and the UN handbook on census editing were reviewed to form the basis of the specifications.

Editing strategy for Census 2011

The Census 2011 questionnaire was very complex, characterised by many sections, interlinked questions and skipping instructions. Editing of such complex, interlinked data items required application of a combination of editing techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle dataset. CSPro software was used to resolve content-related errors. The strategy used for Census 2011 data editing was implementation of automated error detection and correction with minimal changes. Combinations of logical and dynamic imputation were used. Logical imputations were preferred, and in many cases substantial effort was undertaken to deduce a consistent

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value based on the rest of the household’s information. To profile the extent of changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the edited dataset. Imputation flags values include the following:

0 no imputation was performed; raw data were preserved

1 logical editing was performed, raw data were blank

2 logical editing was performed, raw data were not blank

3 hot-deck imputation was performed, raw data were blank

4 hot-deck imputation was performed, raw data were not blank

1.2.6 Independent monitoring and evaluation of census field activities

Independent monitoring of the Census 2011 field activities was carried out by a team of 31 professionals and 381 Monitoring and Evaluation Monitors from Monitoring and Evaluation division. These included field training, publicity, listing and enumeration. This was to make sure that the activities were implemented according to the plans and have independent reports on the same. They also conducted Census 2011 and the Post-enumeration Survey (PES) verification studies to identify the out-of-scope cases within Census 2011 (a sample of 7 220 EAs) and the PES sample (600 EAs) as reported in the Census 2011 PES EA Summary Books.

1.2.7 Post-enumeration survey (PES)

A post-enumeration survey (PES) is an independent sample survey that is conducted immediately after the completion of census enumeration in order to evaluate the coverage and content errors of the census. The PES for Census 2011 was undertaken shortly after the completion of census enumeration, from November to December 2011, in approximately 600 enumeration areas (EAs) (which later increased to 608 due to subdivision of large EAs). The main goal of the PES was to collect high quality data that would be compared with census data in order to determine how many people were missed in the census and how many were counted more than once.

A population census is a massive exercise, and while every effort is made to collect information on all individuals in the country, including the implementation of quality assurance measures, it is inevitable that some people will be missed and some will be counted more than once. A PES assists in identifying the following types of errors:

• Coverage error: this includes both erroneous omissions (e.g. a household that was not enumerated) and erroneous inclusions (e.g. a household that moved into the enumeration area (EA) after census but was still enumerated, or a household that was enumerated more than once).

• Content error: this refers to the errors on the reported characteristics of the people or households enumerated during census.

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The errors may emanate from the following reasons:

• Failure to account for all inhabited areas in the EA frame; • EA boundary problems; • Incomplete listing of structures and failure to identify all dwellings within an EA; • Failure to enumerate/visit all listed dwellings within an EA; • Failure to identify all households within a dwelling unit in instances whereby a

dwelling unit has more than one household; • Failure to enumerate households (complete questionnaires) for all households due to

refusals, unreturned questionnaires for self-enumeration, inability to contact households, etc.);

• Failure to include all individuals within households; • Failure to observe the inclusion rule based on a person’s presence on census night

(i.e. failure to apply the de facto rule accurately); and • Lost questionnaires or damaged questionnaires that could not be processed.

Usually more people are missed during a census, so the census count of the population is lower than the true population. This difference is called net undercount. Rates of net undercount can vary significantly for different population groups depending on factors such as sex, age and geographic location. Stats SA obtains estimates of the net undercount, including the type and extent of content errors (reported characteristics of persons and households enumerated in the census) using information collected through the PES.

Preparations for the PES

Planning involved the development of documents outlining the goal and objectives of the PES, timelines of the project, identification of resources (financial, human and otherwise) required for implementing the project, and the development of methodology documents. Timelines for the PES were synchronised with those of census to ensure the relevance of the project, and adhered to international best practice for maintaining a closed population between Census 2011 and PES data collection, i.e. it should be carried out within a few months, preferably within six (6) months, after the completion of census fieldwork to ensure that the impact of natural population changes, such as births, deaths and migration, as well as lapses in respondent recall do not complicate the exercise. Activities of the PES included the following:

• Sampling: sample design and selection; • Development of data collection methodologies: methods and procedures for data

collection (publicity, listing and enumeration), including quality control measures applied during data collection;

• Development of matching and reconciliation procedures and systems: guidelines for matching, including rules for determining the match status of households and individuals, as well as a computer-based system for capturing household and person records for matching purposes;

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• Questionnaire development: selection of data items which allowed measurement of coverage and content, including layout design and printing of questionnaire;

• Data collection: publicity, listing and enumeration of households in selected enumeration areas (EAs);

• Matching and reconciliation: office matching (comparison) of Census 2011 and PES household and person records, and revisits to households in order to confirm or get more information that might assist in matching unresolved cases; and

• Analysis and reporting: compilation of tables and report on PES results.

Methodology

The PES is an independent survey that replicates the census in sampled enumeration areas (EAs). The major assumption used in the PES is that the census and the PES are independent, the estimate of the percentage missed by the PES but found by the census, and the percentage missed by the census but found by the PES, can be used to construct estimates of the percentage missed by both PES and census. The PES sought to estimate the total number of persons and households in housing units on the night of 9–10 October 2011 (census night). The units of observation were the persons who spent the census night and/or the PES night in these living quarters.

Sampling

The sampling frame for the PES was the complete list of Census 2011 EAs, amounting to 103 576 EAs. The primary sampling units (PSUs) were the census EAs. The principle for selecting the PES sample is that the EA boundaries for sampled EAs should have well-defined boundaries, and these boundaries should correspond with those of census EAs to allow for item-by-item comparison between the census and PES records. The stratification and sampling process followed will allow for the provision of estimates at national, provincial, urban (geography type = urban) and non-urban (geography type = farm and traditional) levels, but estimates will only be reliable at national and provincial levels. The sample of 600 EAs was selected and allocated to the provinces based on expected standard errors which were based on those obtained in PES 2001. Populations in institutions (other than workers’ hostels), floating and homeless individuals were excluded from the PES sample.

Questionnaire development

The approach to questionnaire design focused on capturing the main elements for measuring coverage and content errors. Only a few elements from the Census 2011 questionnaire which were not likely to change within a short period (that is between the census and the PES reference nights) were retained. The questionnaire allowed for the classification of each listed person as ‘non-mover’, ‘in-mover’, ‘out-mover’, or ‘out-of-scope’, with regard to their household presence status on census night (9–10 October 2011). The data items for the PES questionnaire included first name and surname, date of birth, age, sex, population group and presence of person in dwelling unit on census and/or PES night.

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Fieldwork methodology

The PES replicated the census in the sampled EAs, which meant that all methodologies and procedures for data collection were based on census methodologies and procedures. PES fieldwork was split into the following three (3) phases; publicity and listing, enumeration, and mop-up operations.

• Publicity and listing were conducted at the same time. Publicity focused on informing and educating respondents and relevant stakeholders about the purpose of the PES to ensure successful coverage of all dwelling units (DUs) in selected EAs. Listing involved the recording of all structures (including all DUs, number of households in DUs and number of persons in households) in the sampled EAs in the EA Summary Books.

• Enumeration involved interviewing respondents and recording responses in the fields provided in the PES questionnaire. Self-enumeration for the PES was discouraged, but was used in instances where the respondent insisted on self-enumeration.

• Mop-up operations were conducted in the form of follow-up visits by senior field staff to households that could not be contacted during the enumeration period.

Matching and reconciliation methodology

The matching exercise involved the comparison of household and person records in Census 2011 data and PES data. A two-way case-by-case matching was conducted using the two sources: PES questionnaires and Census 2011 questionnaires. Reconciliation visits were conducted in order to confirm or get more information that would assist in matching unresolved cases, i.e. households or individuals enumerated in the census that did not correspond with households or individuals enumerated in the PES. Guidelines for matching, including rules for determining the match status of households and individuals, were developed. A computer-assisted manual matching system was developed for the capturing of data for matching purposes.

PES data collection

PES data collection commenced immediately after the completion of census fieldwork. The PES is a much smaller scale operation (and hence easier to control) than the census. These features enable the PES to deliver a more accurate estimate of the percentage of people and dwellings missed by the census. PES data collection (field operations) was independent from census operations and the following measures were taken to maintain the operational independence of the PES:

• independent listing of enumeration areas (EAs) in the PES sample; • using separate/independent office staff in the PES and census where possible; • ensuring the PES interviewers were not employed as census field staff in the same

area, and vice versa; and • maintaining the confidentiality of the PES sample so that census field and office staff

were not aware which areas are included in the PES.

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Temporary personnel (Fieldworkers and Fieldwork Supervisors) were recruited from the EAs/districts in which they would be working and underwent rigorous training on fieldwork procedures to ensure that they deliver work of high quality at the end of the fieldwork phase. Experienced permanent staff from Household Surveys (based in provincial offices) was seconded to the project for the duration of data collection in supervisory positions to ensure high quality data and minimise costs. The PES followed the integrated approach towards fieldwork; whereby one (1) Fieldworker conducted publicity, listing and enumeration in one (1) EA. A total of 768 Fieldworkers and Fieldwork Supervisors were appointed for the collection of data in the 608 EAs (initially 600, but increased to 608 due to split EAs). A ratio of one (1) Fieldwork Supervisor for four (4) Fieldworkers was applied, but due to the spread of the sample in various districts, this ratio could not always be applied.

Matching and reconciliation

The matching process involved the comparison of household and person records in census data and PES data. The main phases in the matching process were:

• Initial matching involved searching through the census records in order to find the corresponding cases from the PES enumeration records, and vice-versa (a two-way match);

• Capturing involved the capturing of PES and census information on a capturing tool which formed part of the computer-assisted manual matching system. Information for non-matched households and persons was also captured;

• Computer-assisted matching which was the automated assigning of an initial match status for the household and persons, and persons’ moving status. This process was done concurrently with the capturing process. Classifications from initial matching are as follows:

1. matched 2. possible match

In PES not in Census:

3. in PES not in Census – definite non-match 4. in PES not in Census – insufficient or unclear information 5. in-mover 6. born after Census 7. in Census not in PES

• Reconciliation visits are follow-up visits to households in the PES sampled EAs. The purpose of reconciliation visits was to collect relevant information in order to determine the final match status of unresolved cases identified during initial matching. Cases of ‘possible match’, ‘in PES not in Census – insufficient or unclear information’, and ‘in Census not in PES’ were considered unresolved and were sent to the field for reconciliation; and

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1. matched

In PES not in Census:

2. missed in Census 3. PES erroneous inclusion – cases in PES not in Census that were outside the EA

boundaries or otherwise erroneously included in PES 4. PES insufficient information – cases in PES not in Census for which a final match status

cannot be assigned due to insufficient information 5. in-mover 6. born after Census

In Census not in PES:

7. correctly enumerated in Census, missed in PES 8. Census erroneous inclusion 9. Census insufficient information – cases in Census not in PES for which a final match

status cannot be assigned due to insufficient information

• Final matching involved the use of the results obtained from the reconciliation visits and initial matching phases to assign a definite match status to each case. The table below illustrates the outcomes from final matching.

Estimation and tabulation

Coverage measures were calculated only for cases belonging to the PES universe.

The initial estimates – weighted estimates of total from the sample include the following:

a) Estimated number of non-movers; b) Estimated number of out-movers; c) Estimated number of matched non-movers; d) Estimated number of matched out-movers; e) Estimated number of in-movers; f) Estimated number of erroneous inclusions in the census; and g) Estimated number of correctly enumerated persons missed in the PES.

Dual system estimation was used to arrive at the true population of the country. This means that two independent sources or ‘systems’ are used to arrive at the estimate of the true population: the census and the PES. Both estimates contribute to the dual-system estimate, which is more complete than either the census or the PES estimate alone. In the end, this true population is compared with the Census-enumerated population and the difference is the net undercount (or overcount). The following table indicates the undercount rates as estimated by the PES.

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Net Census coverage error: total and rate by province

Province Omission rate for

persons Omission rate for

households Western Cape 18,6 17,8 Eastern Cape 12,9 10,3 Northern Cape 13,4 14,8 Free State 10,1 9,4 KwaZulu-Natal 16,7 16,5 North West 14,9 17,0 Gauteng 14,7 15,2 Mpumalanga 15,5 14,4 Limpopo 10,0 9,6 All provinces 14,6 14,3

The adjustment procedure consisted of creating homogeneous adjustment classes with similar coverage rates and calculating a common undercount rate, adjustment factor and adjustment figure for each class separately. The adjusted figure for the total population was obtained by summing across the adjustment classes. In addition, only the population of households received adjustment classes. The totals for the balance of the population, namely people living in collective quarters and the homeless on the streets, were not adjusted.

1.3 Conclusion

The 2011 Census project had its own challenges and successes, like any other massive project. Be that as it may, the following are worth mentioning: the census fieldworkers who traversed the country to collect information from households and those that we lost in the process. The respondents who opened their doors and locked their dogs to aid the field staff to do their work, the processors who worked 24 hours/7 days a week to ensure that the data can be released within a year of enumeration. The census management team who met daily for two years to steer the project forward, the Stats SA EXCO for the leadership they provided, the Statistics Council and in particular the sub-committee on population and social statistics for their continued guidance and support, and finally the Minister in the Presidency: responsible for planning for the robust interrogation of the plans and guidance on this project. It is through such concerted efforts that, as a country, we can and will continuously improve on our endeavours.

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Chapter 2: Geography of South Africa

2.1 Provincial boundary changes, 2001–2011

A number of changes occurred in terms of provincial and municipal boundaries during the period between Censuses 2001 and 2011. Of the nine provinces, only two provinces (Western Cape and Free State) were not affected by changes. The provincial boundary changes were mostly as a result of eight cross-boundary municipalities which were absorbed in full into respective provinces.

Table 2.1: Geographical land area changes since 2001

Province name Provincial code Land area in square

kilometres 2011 Land area in square

kilometres 2001 Western Cape 1 129 462 129 449 Eastern Cape 2 168 966 169 954 Northern Cape 3 372 889 362 599 Free State 4 129 825 129 824 KwaZulu-Natal 5 94 361 92 305 North West 6 104 882 116 231 Gauteng 7 18 178 16 936 Mpumalanga 8 76 495 79 487 Limpopo 9 125 754 122 816 Total 1 220 813 1 219 602

Note: The shift of the national boundary over the Indian Ocean in the north-east corner of KwaZulu-Natal to cater for the Isimangaliso Wetland Park led to the increase in South Africa's land area.

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Map 2.1: Provincial boundary changes since 2001

Provincial boundary changes mostly affected North West (land size decreased to 11 348,9 square kilometres). Most of this was absorbed by Northern Cape. The second largest decrease in land size was for Mpumalanga which decreased by 2 991,9 square kilometres, with Limpopo being the main recipient of this land area.

It should be noted that the increased extent of KwaZulu-Natal is not mainly based on the exchange of Umzimkulu (formerly in the Eastern Cape) and Matatiele (formerly in KwaZulu-Natal), but due to the shift of the national boundary over the Indian Ocean in the north-east corner of the province to cater for the Isimangaliso Wetland Park. In terms of which areas moved to which province, a detailed outline is provided below.

Northern Cape and North West: • GaSegonyana and Phokwane municipalities were cross-boundary municipalities

between Northern Cape and North West in 2001 and were allocated to Northern Cape in full based on the current provincial boundaries.

• Kagisano municipality (2001) was split into Kagisano/Molopo municipality and Joe Morolong municipality, with the former portion now in North West and the latter now part of the Northern Cape.

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• Moshaweng municipality (now part of Joe Morolong municipality) was incorporated in full into Northern Cape based on the current provincial boundaries.

North West and Gauteng: • Merafong City municipality (2001) was a cross-boundary local municipality between

North West and Gauteng and was allocated to Gauteng based on the current provincial boundaries.

• West Rand (DMA) municipality (2001) was not aligned to the then provincial boundary and was absorbed into Mogale City municipality in full based on the current provincial boundaries.

• City of Tshwane Metropolitan municipality was a cross-boundary municipality between Gauteng and North West. The portions adjacent to Moretele and Madibeng municipalities were allocated to Gauteng in full based on the current provincial boundaries.

North West and Limpopo: • Limpopo lost a portion of the Bela-Bela municipality to North West’s Moretele

municipality. In turn, North West lost a portion of the Moretele municipality to Limpopo’s Bela-Bela municipality based on the current provincial boundaries.

Gauteng and Mpumalanga: • A portion of Delmas municipality (2001), now called Victor Kanye, was allocated to

the City of Tshwane in Gauteng based on the current provincial boundaries. • Kungwini municipality, now incorporated into the City of Tshwane, was a cross-

boundary municipality and is now fully allocated to Gauteng, based on the current provincial boundaries.

Mpumalanga and Limpopo: • Greater Groblersdal (now Elias Motsoaledi), Greater Marble Hall (now Ephraim

Mogale), and Greater Thubatse were cross-boundary municipalities between Mpumalanga and Limpopo, and have now been allocated in full to Limpopo. Ephraim Mogale municipality was absorbed into the Schuinsdraai Nature Reserve.

• Bushbuck Ridge municipality was a cross-boundary municipality between Limpopo and Mpumalanga and has now been allocated in full to Mpumalanga. (Bushbuck Ridge also absorbed a portion of the Kruger Park cross-boundary District Management Area.)

KwaZulu-Natal and Eastern Cape: • Umzimkulu, formerly in Eastern Cape, and Matatiele, formerly in KwaZulu-Natal were

in effect exchanged, with Umzimkulu now being in KwaZulu-Natal and Matatiele now being in Eastern Cape based on the current provincial boundaries.

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2.2 Local municipal boundary changes, 2001–2011

In 2001, the Geographical Frame consisted of 262 local municipalities. This total has been reduced to 234 local municipalities in the 2011 geographical frame. The difference of 28 municipalities is explained as follows:

In total, 25 District Management Areas (DMAs) were absorbed into the existing provinces.

• The City of Tshwane absorbed a further two municipalities (Nokeng Tsa Taemane and Kungwini).

• A new municipality (Kagisano Molopo – NW379) was established by merging NW391 (Kagisano) and NW395 (Molopo).

For municipalities, 107 municipalities decreased in geographical area while 155 municipalities had an increase in geographical area.

Map 2.2: Municipal boundary changes since 2001

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Figure 2.1: Percentage distribution of land area by province, 2011

Comparing Census 2011 with previous censuses

Comparison of Census 2011 with previous censuses (1996 and 2001) required alignment of data for the two censuses to 2011 municipal boundaries. This is because the country’s provincial demarcations underwent changes through a number of changes at provincial and municipal boundaries.

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Chapter 3: Results pertaining to persons 3.1 Introduction

South Africa has had three successful censuses since the first democratic elections in 1994. The first census was conducted in 1996, followed by the second one in 2001 and the third in 2011. In this chapter, data from previous censuses (1996 and 2001) is compared with the data from Census 2011. This section mainly focuses on the demographic characteristics of the population of Gauteng; another part of this section covers migration and labour force.

3.2 Population size and distribution

Table 3.1: Population and percentage share by province, Censuses 1996, 2001 and 2011

Province

1996 2001 2011

Population %

share Population %

share % change

(1996–2001) Population %

share % change

(2001–2011) Western Cape 3 956 875 9,7 4 524 335 10,1 14,3 5 822 734 11,2 28,7 Eastern Cape 6 147 244 15,1 6 278 651 14,0 2,1 6 562 053 12,7 4,5 Northern Cape 1 011 864 2,5 991 919 2,2 -2,0 1 145 861 2,2 15,5 Free State 2 633 504 6,5 2 706 775 6,0 2,8 2 745 590 5,3 1,4 KwaZulu-Natal 8 572 302 21,1 9 584 129 21,4 11,8 10 267 300 19,8 7,1 North West 2 727 223 6,7 2 984 098 6,7 9,4 3 509 953 6,8 17,6 Gauteng 7 834 125 19,3 9 388 854 20,9 19,8 12 272 263 23,7 30,7 Mpumalanga 3 123 869 7,7 3 365 554 7,5 7,7 4 039 939 7,8 20,0 Limpopo 4 576 566 11,3 4 995 462 11,1 9,2 5 404 868 10,4 8,2 South Africa 40 583 573 100,0 44 819 778 100,0 10,4 51 770 561 100,0 15,5

Table 3.1 shows that in both 1996 and 2011, the province with the highest population was KwaZulu-Natal, followed by Gauteng. In 2011, however, Gauteng had the biggest population (12 272 263) compared to other provinces, followed by KwaZulu-Natal with a population of 10 267 300. Its share has increased from 19,3% in 1996 to 23,7% in 2011. The province also recorded the highest percentage change between 2001 and 2011 (30,7%) followed by Western Cape and Mpumalanga (28,7% and 20% respectively).

Table 3.2: Population size by province and population group, 2011

Provinces Population group

Black African Coloured Indian/Asian White Western Cape 1 912 547 2 840 404 60 761 915 053 Eastern Cape 5 660 230 541 850 27 929 310 450 Northern Cape 576 986 461 899 7 827 81 246 Free State 2 405 533 83 844 10 398 239 026 KwaZulu-Natal 8 912 921 141 376 756 991 428 842 North West 3 152 063 71 409 20 652 255 385 Gauteng 9 493 684 423 594 356 574 1 913 884 Mpumalanga 3 662 219 36 611 27 917 303 595 Limpopo 5 224 754 14 415 17 881 139 359 South Africa 41 000 937 4 615 402 1 286 930 4 586 840

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Table 3.2 indicates that the black African group represented the largest proportion of the population in most provinces in South Africa. It was only in the Western Cape where the coloured population is the largest group. This group constituted 77,9% of the population in the province, followed by whites (15,7%) as shown in Figure 3.1.

Figure 3.1: Percentage distribution of the population groups in Gauteng, Censuses 1996, 2001 and 2011

* Excludes ‘Other’.

Figure 3.1 shows that the black African population group in Gauteng has the highest proportion in all censuses. However, the white population group decreased from 22% in 1996 to 18,8% in 2001, and to 15,7% in 2011.

Table 3.3: Population size and percentage share by district municipalities, Censuses 1996, 2001 and 2011

District municipalities

1996 2001 2011

Population % Population % 1996–2001

% change Population % 2001–2011

% change Sedibeng 716 844 9,2 794 088 8,5 10,8 916 484 7,5 15,4 West Rand 659 475 8,4 744 627 7,9 12,9 820 995 6,7 10,3 Ekurhuleni 2 026 978 25,9 2 481 762 26,4 22,4 3 178 470 25,9 28,1 City of Johannesburg 2 638 471 33,7 3 226 055 34,4 22,3 4 434 827 36,1 37,5 City of Tshwane 1 792 357 22,9 2 142 322 22,8 19,5 2 921 488 23,8 36,4 Gauteng 7 834 125 100,0 9 388 854 100,0 19,8 12 272 263 100,0 30,7

Black African Coloured Indian/Asian White1996 72,3 3,6 2,1 22,02001 75,2 3,6 2,3 18,82011 77,9 3,5 2,9 15,7

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

%

Population groups in Gauteng

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Table 3.3 provides the size and percentage share of the total in two periods (1996–2001 and 2001–2011) by district municipality. Most of the population reside in three district municipalities (Ekurhuleni, City of Johannesburg and City of Tshwane). This profile has persisted since 1996. In most district municipalities percentage change increased, except in the West Rand where the percentage decreased from 12,9% between 1996–2001 to 10,3% in 2001–2011.

Table 3.4: Population size by district municipalities and population group, 2011

District municipalities

Population group Black

African Coloured Indian/Asian White Black

African Coloured Indian/ Asian White

Total % Sedibeng 555 513 6 855 5 662 144 111 82,1 1,2 1,0 15,7 West Rand 503 461 15 561 5 639 130 026 79,6 2,5 1,2 16,8 Ekurhuleni 1 478 274 56 019 28 390 450 845 79,2 2,7 2,2 15,9 City of Johannesburg 1 853 211 171 126 96 821 492 484 77,1 5,6 4,9 12,4 City of Tshwane 1 229 822 30 673 24 917 493 548 75,9 2,0 1,9 20,2 Gauteng 5 620 280 280 235 161 429 1 711 014 77,9 3,5 2,9 15,7

* Excludes ‘Other’.

Table 3.4 indicates that the black African population group has the highest proportion of over 75,0% in all district municipalities. The highest proportion of whites resides in the City of Tshwane, while the highest proportion of coloureds and Indians/Asians reside in the City of Johannesburg.

Figure 3.2: Distribution of the Gauteng population by age group, 2011

4,9

3,7

3,3

3,7

5,7

6,3

5,3

4,4

3,4

2,7

2,2

1,7

1,2

0,7

0,5

0,3

0,2

0,1

4,8

3,7

3,3

3,8

5,5

5,8

4,6

3,9

3,2

2,8

2,4

1,9

1,3

0,9

0,7

0,5

0,3

0,2

8,0 6,0 4,0 2,0 0,0 2,0 4,0 6,0 8,0

0 - 4

5 - 9

10 - 14

15 - 19

20 - 24

25 - 29

30 - 34

35 - 39

40 - 44

45 - 49

50 - 54

55 - 59

60 - 64

65 - 69

70 - 74

75 - 79

80 - 84

85+

Female_GP

Male_GP

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Census 2011 Provincial Profile: Gauteng, Report 03-01-76

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Figure 3.2 shows higher percentages of people in the age groups 20–34 than those aged 0–19. The age group 25–29 is higher in percentage compared to other age groups. The age group 15–64 is approximately 73,0% of the Gauteng population. This indicates that Gauteng is the province for the working age group.

Table 3.5: Percentages and dependency ratios

District municipalities

0-14 15-64 65+ 0-14 15-64 65+

Child (0-14)

depen-dency ratio

Adult (65+)

depen-dency ratio

Total depen-dency ratio

Total % Dependency ratio Sedibeng 232 410 637 221 46 851 25,4 69,5 5,1 36,5 7,4 43,8 West Rand 198 135 589 971 32 886 24,1 71,9 4,0 33,6 5,6 39,2 Ekurhuleni 772 464 2 279 451 126 552 24,3 71,7 4,0 33,9 5,6 39,4 City of Johannesburg 1 028 811 3 222 606 183 408 23,2 72,7 4,1 31,9 5,7 37,6 City of Tshwane 677 109 2 101 473 142 905 23,2 71,9 4,9 32,2 6,8 39,0 Gauteng 2 908 932 8 830 725 532 608 23,7 72,0 4,3 32,9 6,0 39,0

Table 3.5 indicates that Sedibeng has the highest child, adult and total dependency ratios compared to all other district municipalities. This is also reflected by the lowest proportion of persons aged 15–64 years.

Figure 3.3: Dependency ratio by district municipalities, Censuses 1996, 2001 and 2011

Figure 3.3 shows the total dependency ratio for Censuses 1996, 2001 and 2011 by district municipalities. In the West Rand, the total dependency ratio increased from 34,3% in 1996 to 36,4% and to 39,2% in 2011. While all other district municipalities recorded a decrease, the highest decrease was recorded in the City of Tshwane.

Sedibeng West Rand EkurhuleniCity of

Johannesburg

City ofTshwane Gauteng

Total dependency ratio_1996 48,0 34,3 43,2 41,1 46,6 43,7

Total dependency ratio_2001 42,9 36,4 39,1 36,6 40,9 38,7

Total dependency ratio_2011 43,8 39,2 39,4 37,6 39,0 39,0

0,0

10,0

20,0

30,0

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50,0

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follo

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by

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(16,

9%).

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Census 2011 Provincial Profile: Gauteng, Report 03-01-76

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3.4 Migration

Figure 3.4: Percentage distribution of migrants in Gauteng, 2011

Figure 3.4 shows that the largest percentage of migrants into Gauteng was from outside South Africa (31,4%), followed by Limpopo (18,3%) and KwaZulu-Natal (11,9%).

Figure 3.5: Net loss or gain of people in each province through inter-provincial migration, South Africa, 2001 and 2011

Figure 3.5 shows that Gauteng gained the highest number of people from other provinces, followed by the Western Cape, and both provinces are the only ones that gained people from other provinces during that period. North West also gained people in 2011 compared to 2001. Eastern Cape lost the most people, followed by Limpopo.

3,3

7,6

1,0

4,8

11,9

6,77,9

18,3

31,4

0,2

7,0

0,0

5,0

10,0

15,0

20,0

25,0

30,0

35,0

%

Percentage distribution of migrants in GP (Gauteng)

Percentage distribution ofthe migrants inGP(Gauteng)

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Figure 3.6: Percentage distribution of internal migrants by 5-year age group

Figure 3.6 shows the percentage of internal migrants in South Africa and Gauteng by 5-year age group. Close to 22% of the age group 25–29 years are internal migrants and less than 3% of the age group 65+ years are internal migrants in Gauteng; 56,2% and 63,2% of the youth (15–34 years) are internal migrants in South Africa and Gauteng, respectively.

Figure 3.7: Net loss or gain by gender in each province through inter-provincial migration, Census 2011

Figure 3.7 shows that Gauteng had the highest number of male and female migrants, followed by Western Cape. However, in North West it appears that the province gained more male than female migrants. Mpumalanga lost more females than males.

0 -4

5-910 -14

15 -19

20 -24

25 -29

30 -34

35 -39

40 -44

45 -49

50 -54

55 -59

60 -64

65 -69

70 -74

75 -79

80 -84

85+

Percentage of Internal Migrants by agegroup in GP 3,8 4,1 4,5 7,1 20,1 21,8 13,9 8,6 5,3 3,6 2,4 1,6 1,1 0,7 0,6 0,4 0,2 0,2

Percentage of Internal Migrants by agegroup in SA 4,1 4,8 5,3 7,4 17,0 18,9 12,9 8,9 6,0 4,2 3,1 2,3 1,8 1,3 0,9 0,5 0,3 0,2

0,0

5,0

10,0

15,0

20,0

25,0

%Percentage of the internal migrants by 5-year age group

WC EC NC FS KZN NW GP MP LP

Net-migration Female 93,656 -156,767 -10,327 -28,349 -43,733 4,084 273,814 -14,104 -118,274

Net-migration Male 96,297 -158,758 -3,788 -30,431 -63,608 26,687 276,94 -7,003 -136,336

-200

-150

-100

-50

0

50

100

150

200

250

300

Nu

mb

er (

'000

)

Net migration by gender

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Figure 3.8: Net migration by gender for the age group 0–14

Figure 3.8 shows the net migration by gender for the age group 0–14 years. Gauteng has the highest net migration for both males and females, followed by Western Cape. However, Eastern Cape and Limpopo show a decrease in net migration.

Figure 3.9: Net migration by gender for the age group 15–64

Figure 3.9 shows the net migration by gender for age group 15–64. Gauteng has the highest net migration for both males and females, followed by Western Cape. However, Eastern Cape and Limpopo show a decrease in net migration.

WC EC NC FS KZN NW GP MP LP

Net Migration Female 7,764 -14,532 -1,107 -3,168 -4,965 2,1 24,174 -0,318 -9,948

Net Migration Male 6,828 -12,942 -0,804 -3,177 -5,376 2,865 22,332 0,027 -9,753

-20

-15

-10

-5

0

5

10

15

20

25

30

Nu

mb

er (

'000

)Net migration by gender for the age group 0-14 years

WC EC NC FS KZN NW GP MP LP

Net Migration Female 80,868 -141,41 -8,379 -24,372 -37,875 1,479 250,896-13,506 -107,7

Net Migration Male 85,407 -146,46 -2,493 -26,763 -57,918 23,133 258,567 -6,924 -126,55

-200

-150

-100

-50

0

50

100

150

200

250

300

Nu

mb

er (

000)

Net migration by gender for the age group 15-64 years

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Figure 3.10: Net migration by gender for the age group 65+

Figure 3.10 shows the net migration by gender for the age group 65+. Western Cape has the highest net migration of the adult population 65+, followed by North West. However, Gauteng has the lowest net migration of the adult population compared to other provinces.

3.5 General health and functioning

Table 3.7: Distribution of population aged 5 years and older by disability status, sex, numbers and percentages

Province and district

Disability status

Male Female Total Male Female Total

Number Number Number % % %Sedibeng Not disabled 314 295 318 673 632 968 93,3 91,5 92,4

Disabled 22 497 29 738 52 235 6,7 8,5 7,6Total 336 792 348 411 685 203 100,0 100,0 100,0

West Rand Not disabled 294 066 270 552 564 618 94,0 92,3 93,2Disabled 18 813 22 621 41 434 6,0 7,7 6,8Total 312 879 293 173 606 052 100,0 100,0 100,0

Ekurhuleni Not disabled 1 153 303 1 092 133 2 245 436 95,3 93,7 94,5Disabled 56 745 73 533 130 278 4,7 6,3 5,5Total 1 210 048 1 165 666 2 375 714 100,0 100,0 100,0

City of Johannesburg

Not disabled 1 578 111 1 568 234 3 146 345 96,2 94,7 95,4Disabled 63 166 87 387 150 553 3,8 5,3 4,6Total 1 641 277 1 655 621 3 296 898 100,0 100,0 100,0

City of Tshwane Not disabled 1 009 499 1 028 553 2 038 052 95,4 94,3 94,8Disabled 48 761 62 070 110 831 4,6 5,7 5,2Total 1 058 260 1 090 623 2 148 883 100,0 100,0 100,0

Gauteng Not disabled 4 349 274 4 278 145 8 627 419 95,4 94,0 94,7Disabled 209 982 275 349 485 331 4,6 6,0 5,3Total 4 559 256 4 553 494 9 112 750 100,0 100,0 100,0

WC EC NC FS KZN NW GP MP LP

Net Migration Female 5,022 -0,828 -0,834 -0,816 -0,891 0,501 -1,254 -0,273 -0,627

Net Migration Male 4,074 0,645 -0,51 -0,498 -0,306 0,696 -3,954 -0,105 -0,042

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

Nu

mb

er (

'000

)Net migration by gender for the age group 65+ years

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Table 3.7 above shows that 5,3% of the population in the province were disabled. The females recorded the highest proportion (6%) compared with males (4,6%). This gender disparity was recorded in all district municipalities. The City of Johannesburg is the only district with lower proportions as compared to others, with 4,6% of its population living with disability. The sex disparity shows that 3,8% of males in the City of Johannesburg are disabled as compared to females (5,3%). The highest proportion of persons with disability were recorded in Sedibeng and West Rand (7,6% and 6,8% respectively).

3.6 Education

Figure 3.11: Percentage of persons aged 5–24 who were attending an educational institution in Gauteng, Censuses 2001 and 2011

The results in Figure 3.11 show that there was a general increase in the proportion of persons attending an educational institution between 2001 and 2011, particularly for those aged 5–11 years. The highest increase was recorded for those aged 5–6 years. The proportions fall below 50% after age 19. This indicates high dropout rates across these ages.

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Census 2001 52,1 70,6 90,2 94,7 95,3 94,5 94,7 96,4 96,0 95,4 93,2 90,4 84,7 72,7 57,6 45,5 34,1 24,2 18,1 13,3

Census 2011 82,0 92,7 96,5 97,1 97,4 97,0 97,2 96,2 96,1 96,0 94,8 92,4 87,2 74,3 56,7 44,9 34,5 26,2 20,1 16,2

0,0

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60,0

70,0

80,0

90,0

100,0

%

Percentage of persons aged 5-24 attending an educational institution in Gauteng

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Figure 3.12: Attendance at an educational institution by gender in Gauteng, Census 2001

Figure 3.12 shows that the proportion of females and males aged 5–24 years attending an educational institution were the same across all ages in 2001. This indicates that there were no significant gender disparities. The same trend is also depicted by the findings from Census 2011 as shown in Figure 3.13 below.

Figure 3.13: Attendance at an educational institution by gender in Gauteng, Census 2011

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Female_ Census 2001 52,4 70,6 90,4 94,8 95,4 94,7 95,0 96,5 96,3 95,5 93,2 90,0 83,7 70,9 56,1 44,8 33,8 24,0 18,1 13,5

Male_ Census 2001 51,8 70,5 89,9 94,6 95,2 94,3 94,4 96,2 95,8 95,3 93,3 90,8 85,7 74,5 59,0 46,2 34,5 24,4 18,1 13,2

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

%Attendance at an educational institution by gender in Gauteng, Census 2001

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Female_ Census 2011 81,9 92,6 96,4 97,0 97,4 96,9 97,2 96,2 96,2 96,1 95,0 92,6 87,3 75,6 57,7 44,6 33,5 25,3 19,5 15,5

Male_ Census 2011 82,0 92,9 96,5 97,2 97,5 97,1 97,1 96,2 96,0 96,0 94,6 92,2 87,0 73,0 55,7 45,1 35,6 27,0 20,8 17,0

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

%

Attendance at an educational institution by gender in Gauteng, Census 2011

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Figure 3.14: Percentage of persons aged 5–24 years currently attending an educational institution by type of institution, Censuses 2001 and 2011

Figure 3.14 indicates that the majority of the population aged 5–24 years in Gauteng attend public education institutions. However, the proportion who attend private education institutions has increased for males, from 12,3% in 2001 to 16,3%, and similarly for females from 12,9% in 2001 to 17% in 2011. The City of Johannesburg has continued to record the highest proportion attending these institutions for both sexes.

Figure 3.15: Highest level of education attained by persons aged 20 years and older by gender, Censuses 2001 and 2011

Male Female Male Female Male Female Male Female

Public(government)

PrivatePublic

(government)Private

2001 2011

Sedibeng 91,6 91,2 8,4 8,8 89,1 88,4 10,9 11,6

West Rand 90,8 90,5 9,2 9,5 87,9 87,7 12,1 12,3

Ekurhuleni 88,8 88,2 11,2 11,8 86,1 85,2 13,9 14,8

City of Johannesburg 83,0 82,1 17,0 17,9 78,9 78,1 21,1 21,9

City of Tshwane 90,1 89,7 9,9 10,3 84,8 84,0 15,2 16,0

Gauteng 87,7 87,1 12,3 12,9 83,7 83,0 16,3 17,0

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

%

Percentage of persons aged 5-24 years currently attending an educational institution by type of institution, Censuses 2001 and 2011

Noschooling

Someprimary

Completedprimary

Somesecondary

Grade12/Std 10

Higher

2001 Male 8,3 12,1 5,6 33,6 28,1 12,3

2001 Female 9,1 10,9 5,5 34,9 27,3 12,3

2011 Male 3,5 7,6 3,5 33,4 34,3 17,7

2011 Female 3,9 7,3 3,3 32,5 34,5 18,6

0,05,0

10,015,020,025,030,035,040,0

%

Highest level of education attained by persons aged 20 years and older by gender

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Figure 3.15 shows that the proportion of persons aged 20 years who have no schooling halved, for males from 8,3% in 2001 to 3,5% in 2011 and for females from 9,1% in 2001 to 3,9% in 2011. The proportion of persons who have some primary level education and those who have completed primary level has decreased for both sexes. There was a significant increase in the proportion of persons who completed higher education.

Figure 3.16: Highest level of education attained by persons aged 20 years and older by population group, Censuses 2001 and 2011

Figure 3.16 shows that there was a general decrease in proportion of persons aged 20 years and older who have no schooling, some primary, complete primary and some secondary level education; there was an increase in the proportion who have completed higher education across all population groups since 2001. However, significant disparities still prevail. Black Africans have continued to record the highest proportion with no schooling and the lowest proportion with higher level of education compared with those recorded by other population groups.

3.7 Employment

Employment status of persons aged 15–64 years provides insights into how a country makes effective use of this group. This has a direct link to the overall living standard of the population and a country's economic development. Hence this section focuses on the level of unemployment rates.

BlackAfrican Coloured Indian/Asian White Black Coloured Indian/Asian White

2001 2011

No schooling 11,0 5,5 3,5 1,5 4,5 1,6 2,0 0,6

Some primary 14,8 6,7 4,7 1,3 9,1 4,4 3,8 1,2

Complete primary 7,0 5,0 3,0 0,8 4,0 2,9 2,1 0,7

Some secondary 36,7 44,8 26,0 24,4 36,6 38,1 19,0 18,9

Std 10/Grade 12 23,9 30,0 39,6 39,6 33,4 37,8 38,0 38,0

Higher 6,6 8,0 23,2 32,4 12,4 15,3 35,1 40,7

0,05,0

10,015,020,025,030,035,040,045,050,0

%

Highest level of education attained by persons aged 20 years and older by population group, Censuses 2001 and 2011

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Figure 3.17: Unemployment rate by population group, Census 2011

Figure 3.17 shows that the unemployment rate among the black African population group is highest, while among the white population group it is the lowest.

Figure 3.18: Unemployment rate (official) by sex and age group

0,0

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20,0

25,0

30,0

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40,0

Black African Coloured Indian/Asian White

%

Population groups

Unemployment rate by population group, Census 2011

Unemployment rate(Official)

Unemployment rate(Expanded)

0

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20

30

40

50

60

70

15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64

%

Age groups

Unemployment rate (official) by sex and age group

Male Unemployment rate

Female Unemployment rate

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Figure 3.19: Unemployment rate (expanded) by sex and age group, Gauteng, Census 2011

Based on the official and expanded definition of employment, Figures 3.18 and 3.19 indicate that in all age groups, the unemployment rates among women are higher than those among men. The highest rates are recorded by those aged less than 25 years. The rates generally decline with increasing age as shown in Figures 3.20 and 3.21 below.

Figure 3.20: Unemployment rate (official and expanded) by sex and age group in Gauteng, Census 2011

0

10

20

30

40

50

60

70

80

15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64

%

Age groups

Unemployment rate (expanded) by sex and age group, Gauteng, Census 2011

Male Unemployment rate

Female Unemployment rate

0

10

20

30

40

50

60

70

80

15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64

%

Unemployment rate (official and expanded) by sex and age group in Gauteng, Census 2011

Male Unemployment rate(Official)

Male Unemploymentrate(Expanded)

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Figure 3.21: Unemployment rate (official and expanded) by sex and age group in Gauteng, Census 2011

0

10

20

30

40

50

60

70

80

90

15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64

%

Unemployment rate (official and expanded) by sex and age group in Gauteng, Census 2011

Female Unemploymentrate(Official)

Female Unemploymentrate(Expanded)

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Chapter 4: Results pertaining to households

4.1 Introduction

This chapter concentrates on the characteristics of the dwelling units in which households live and their access to certain services and facilities. This provides an important indication of the well-being of members of the household.

4.2 Households

Table 4.1: Distribution of households by district/local municipalities, Censuses 1996, 2001 and 2011

District and local municipalities

Number of households % 1996 2001 2011 1996 2001 2011

Sedibeng 182 570 224 966 279 768 8,8 8,1 7,2 Emfuleni 149 360 186 926 220 135 81,8 83,1 78,7 Midvaal 17 508 19 573 29 965 9,6 8,7 10,7 Lesedi 15 701 18 467 29 668 8,6 8,2 10,6 West Rand 152 256 207 793 267 397 7,4 7,4 6,8 Mogale City 62 191 85 194 117 373 40,8 41,0 43,9 Randfontein 27 381 36 165 43 299 18,0 17,4 16,2 Westonaria 23 173 30 098 40 101 15,2 14,5 15,0 Merafong City 39 512 56 336 66 624 26,0 27,1 24,9 Ekurhuleni 542 719 745 576 1 015 465 26,2 26,7 26,0 City of Johannesburg 732 845 1 006 910 1 434 856 35,4 36,1 36,7 City of Tshwane 459 122 60 6025 911 536 22,2 21,7 23,3 Gauteng 2 069 512 2 791 270 3 909 022

Table 4.1 shows the number and percentage distribution of the households for both district and local municipalities. There is a notable increase in the number of households in all the municipalities. However, the percentage shares of Sedibeng and West Rand district municipalities have decreased while those of the Cities of Johannesburg and Tshwane have slightly increased since 2001.

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Table 4.2: Headship by district/local municipalities

District and local municipality

Gender of household head Male Female

Sedibeng 65,3 34,7 Emfuleni 63,7 36,3 Midvaal 73,7 26,3 Lesedi 68,7 31,3 West Rand 68,9 31,1 Mogale City 68,8 31,2 Randfontein 66,4 33,6 Westonaria 69,4 30,6 Merafong City 70,6 29,4 Ekurhuleni 68,7 31,3 City of Johannesburg 63,8 36,2 City of Tshwane 64,2 35,8 Gauteng 65,7 34,3

Table 4.2 indicates that more than 63% of the households are headed by males in all the municipalities. The highest proportion was recorded in Midvaal municipality (73,7%) in Sedibeng, followed by Merafong municipality in West Rand.

Figure 4.1: Average households size by municipalities, Censuses 1996, 2001 and 2011

Figure 4.1 shows the average household’s size in Gauteng municipalities for censuses 1996, 2001 and 2011. In Sedibeng (D42), West Rand (DC48), Ekurhuleni (EKU), City of Johannesburg (JHB) and City of Tshwane (TSH), the average household size has decreased.

DC42 GT421 GT422 GT423 DC48 GT481 GT482 GT483 GT484 EKU JHB TSH Gauteng

Average household size_1996 4 4 3 4 3,5 3,5 3,6 3,2 3,6 3,6 3,5 3,7 3,6

Average household size_2001 3,5 3,5 3,1 3,7 3,2 3,2 3,3 3 3,1 3,2 3,1 3,4 3,2

Average household size_2011 3,2 3,2 3 3,2 2,9 2,9 3,2 2,6 2,7 3 2,9 3 3

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

Ave

rag

e

Average household size by municipalities, Censuses 1996, 2001 and 2011

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Figure 4.2: Percentage distribution of households by type of main dwelling, Censuses 1996, 2001 and 2011

Figure 4.2 shows that there has been an increase in the proportion of households living in formal dwellings and a decrease in the proportion of households living in informal dwellings in all district municipalities.

1996 2001 2011 1996 2001 2011 1996 2001 2011 1996 2001 2011

Formal dwelling Informal dwelling Traditionaldwelling Other

DC42: Sedibeng 75,0 81,5 84,8 22,3 16,6 14,3 1,9 1,6 0,3 0,8 0,3 0,6DC48: West Rand 66,6 66,8 72,7 32,0 31,4 25,3 1,1 1,5 0,3 0,4 0,3 1,7EKU: Ekurhuleni 70,2 70,0 77,4 29,1 28,6 21,5 0,4 1,1 0,2 0,2 0,3 0,8JHB: City of Johannesburg 77,8 77,4 81,4 21,5 21,1 17,4 0,4 1,2 0,4 0,2 0,3 0,8TSH: City of Tshwane 78,3 74,9 80,7 20,1 23,0 18,0 1,4 1,7 0,4 0,2 0,3 0,9Gauteng 74,9 74,4 79,8 24,1 23,9 18,9 0,8 1,3 0,4 0,3 0,3 0,9

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10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

%

Percentage distribution of households by type of main dwelling

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Figure 4.3: Percentage distribution of households by tenure status, Censuses 2001 and 2011

Figure 4.3 shows that the proportion of households with rented housing tenure status is increasing in all district municipalities. The proportion is decreasing for those households who owned but not yet paid off their houses in all district municipalities.

2001 2011 2001 2011 2001 2011 2001 2011

Rented Owned but not yetpaid off Occupied rent-free Owned and fully paid

off

Sedibeng 23,6 27,9 25,0 13,4 15,5 20,4 31,8 35,5

West Rand 26,7 41,1 19,9 11,7 18,4 20,3 22,9 24,3

Ekurhuleni 22,4 37,3 26,6 16,7 16,3 16,2 31,9 27,3

City of Johannesburg 29,2 41,2 24,6 16,5 14,8 15,9 28,0 23,8

City of Tshwane 17,1 32,0 26,3 18,3 13,0 13,4 38,9 33,7

Gauteng 23,6 37,1 25,0 16,4 15,5 16,0 31,8 27,9

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10,0

15,0

20,0

25,0

30,0

35,0

40,0

45,0

%

Percentage distribution of households by tenure status

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Figure 4.4: Percentage distribution of households by mode of refuse removal by district municipalities, Censuses 1996, 2001 and 2011

Figure 4.4 shows that the proportion of households whose refuse is removed by the local authority weekly has increased consistently in all district municipalities.

Figure 4.5: Percentage distribution of households by type of toilet facility, Censuses 1996, 2001 and 2011

1996 2001 2011 1996 2001 2011 1996 2001 2011 1996 2001 2011 1996 2001 2011Removed by

local authorityat least once a

week

Removed bylocal authority

less often

Communalrefuse dump

Own refusedump

No rubbishdisposal

Sedibeng 54,3 48,3 88,2 3,7 2,6 1,2 13,8 10,4 1,6 22,2 30,4 6,6 5,8 8,3 1,9West Rand 77,8 76,0 76,8 4,3 2,5 2,7 3,6 2,2 4,2 11,2 15,8 12,1 3,1 3,6 3,6 Ekurhuleni 86,5 87,9 88,4 2,2 0,9 1 2,3 1,8 1,8 5,6 6,6 6 3,3 2,7 2,5City of Johannesburg 88,4 91,2 95,3 5,7 2,7 1,6 1,8 1,4 1 2,6 3,5 1,3 1,3 1,3 0,5City of Tshwane 74,1 76,3 80,7 2,1 2,4 1,3 2,7 1,6 2,2 17,4 15,8 11,9 3,7 4,0 3,3Gauteng 80,9 82,5 88,3 3,7 2,1 1,4 3,3 2,3 1,8 9 10,1 6,1 2,9 3,0 2

0102030405060708090

100

%

Percentage distribution of households by mode of refuse removal

1996 2001 2011 1996 2001 2011 1996 2001 2011 1996 2001 2011Flush or

Chemical toilet Pit toilet Bucket toilet None

Sedibeng 75,5 83,8 89,4 20,3 11,6 7,6 2,5 2,0 1,1 1,8 2,6 1,1West Rand 76,9 73,5 82,3 16 19,0 13,4 3,2 3,3 1,8 3,8 4,2 1,7 Ekurhuleni 84 83,0 87,6 10,5 10,9 7,9 1,0 0,9 2,3 4,5 5,2 1,2City of Johannesburg 86,9 86,5 90,5 7,1 6,8 6,0 4,5 3,8 2,0 1,5 2,8 0,8City of Tshwane 74 71,5 79,4 24,3 24,5 17,4 0,5 0,9 1,0 1,3 3,1 1,3Gauteng 81,5 81,2 86,5 13,6 13,0 9,8 2,4 2,2 1,8 2,4 3,6 1,1

0102030405060708090

100

%

Percentage distribution of households by type of toilet facility

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Figure 4.5 indicates that the proportion of households that have access to flush or chemical toilets in Gauteng increased to 86,5% in Census 2011, from 81,2% in Census 2001 and 81,5% in Census 1996.

Figure 4.6: Percentage distribution of households by type of energy used for cooking by district municipalities, Censuses 1996, 2001 and 2011

Figure 4.6 shows that the proportion of households using electricity for cooking increased from 72,3% in 1996 to 83,9% in 2011. This upward trend is depicted by all district municipalities. The proportion of households using wood and coal as sources of energy for cooking decreased from 1,0%, 0,8%, 0,6% and 4,4%, 2,7% and 0,4% in Censuses 1996, 2001 and 2011 respectively.

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Figure 4.7: Percentage distribution of households by type of energy used for lighting, Censuses 1996, 2001 and 2011

Figure 4.7 shows that the proportion of households using electricity for lighting has increased since 1996. The proportion of households using candles for lighting is decreasing in all district municipalities.

1996 2001 2011 1996 2001 2011 1996 2001 2011 1996 2001 2011 1996 2001 2011Electricity Gas Paraffin Candles Other

Sedibeng 79,2 86,1 90,6 0,2 0,1 0,2 1,4 1,1 1,6 19,2 12,4 7,3 0,0 0,1 0,2West Rand 69,1 69,8 81,7 0,2 0,2 0,2 4,7 4,1 4,4 25,9 25,6 13,2 0,0 0,2 0,2 Ekurhuleni 75,4 74,8 82,2 0,3 0,2 0,3 3 3,9 4,6 21,3 20,7 12,5 0,0 0,2 0,2City of Johannesburg 85,9 84,9 90,8 0,1 0,2 0,2 2,1 2,5 1,7 11,8 12,2 6,9 0,0 0,1 0,2City of Tshwane 77,4 79,9 88,6 0,1 0,2 0,2 1,9 2,5 1,5 20,6 17,2 9,3 0,0 0,1 0,2Gauteng 79,4 80,1 87,4 0,2 0,2 0,2 2,4 2,9 2,6 18,0 16,6 9,4 0,0 0,1 0,2

0

10

20

30

40

50

60

70

80

90

100

%

Percentage distribution of households by type of energy used for lighting

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Figure 4.8: Percentage distribution of households by type of energy used for heating, Censuses 1996, 2001 and 2011

Figure 4.8 shows that the proportion of households using electricity for heating has increased since 1996. The proportion of households using coal for heating has decreased in all district municipalities.

1996 2001 2011 1996 2001 2011 1996 2001 2011 1996 2001 2011 1996 2001 2011 1996 2001 2011 1996 2001 2011

Electricity Gas Paraffin Wood Coal Animal dung Other

Sedibeng 66 71,2 79,4 1 1,2 3,7 7,1 8,2 3,2 5,0 2,7 3,6 20,8 13,0 2,7 0,1 0,2 0,1 0,0 3,2 0,2

West Rand 63,3 58,6 68,8 1,5 1,2 3,2 18,2 21,2 8,9 5,1 6,3 6,7 11,8 8,6 1,4 0,0 0,2 0,1 0,0 3,7 0,2

Ekurhuleni 62,5 61,7 65,6 1,3 1,6 4,3 11,4 13,3 9,4 2,0 1,5 2,7 22,6 19,1 6,6 0,0 0,1 0,1 0,1 2,5 0,3

City of Johannesburg 81,2 76,9 82,1 1,1 1,6 3,7 8,5 11,2 4,7 1,6 1,7 1,9 7,6 6,3 0,9 0,0 0,1 0,1 0,0 2,1 0,3

City of Tshwane 72,5 69,3 73,5 0,7 1,0 3,7 9,3 12,6 4,7 3,6 3,9 4,3 13,7 9,6 0,8 0,0 0,2 0,1 0,1 3,3 0,4

Gauteng 71,7 69,4 74,7 1,1 1,4 3,8 10,0 12,6 6,1 2,7 2,5 3,1 14,3 11,1 2,5 0,0 0,1 0,1 0,1 2,7 0,3

0

10

20

30

40

50

60

70

80

90

%

Percentage distribution of households by type of energy used for heating

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Figure 4.9: Percentage of households that have access to piped (tap) water, Censuses 2001 and 2011

Figure 4.9 indicates an increase in the proportion of households which had access to piped water, and this has significantly increased since 2001.

Figure 4.10: Percentage of households with household goods in working order, Censuses 2001 and 2011

Figure 4.10 shows the proportion of households owning cellphones increased to above 90,0% in Census 2011 in all district municipalities, while the proportion of the households using radio and landline telephones is decreasing.

Sedibeng West Rand Ekurhuleni City ofJohannesburg

City ofTshwane Gauteng

Piped (tap) water inside dwelling/ yard 88,3 79,2 81,9 84,5 79,7 82,7

Piped (tap) water inside dwelling/ yard 92,8 83,2 87,1 91,6 89,2 89,4

70,0

75,0

80,0

85,0

90,0

95,0

%

Percentage of households that have access to piped (tap) water

2001 2011 2001 2011 2001 2011 2001 2011 2001 2011 2001 2011

Cell-phone Radio Computer Refrigerator Television Telephone

Sedibeng 34,9 91,4 78,4 72,7 8,7 25,7 65,4 77,5 65,2 81,3 27,6 13,2

West Rand 38,1 91,5 72,3 66,2 8,9 20,8 53,8 63,5 58,9 74,2 25,3 12,7

Ekurhuleni 42,2 93,3 74,9 67,1 12,3 25,8 58,5 68,0 62,1 77,3 28,9 15,3

City of Johannesburg 46,5 94,4 78,1 71,4 16,2 33,6 63,1 74,6 67,9 83,8 34,1 21,2

City of Tshwane 50,4 95,0 79,5 70,6 18,4 37,6 68,8 76,8 70,2 81,8 35,6 18,9

Gauteng 44,7 93,8 77,2 69,8 14,5 31,1 62,6 72,8 66,0 80,8 31,9 18,0

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

%

Percentage of households with household goods in working order

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Chapter 5: Discussion and conclusion

5.1 Background

Gauteng is the smallest province in the country, occupying an area 16 936 square kilometres, which is approximately 1,4% of the land area of South Africa. It accommodates almost 23,7% of the total South African population, making it the leading province in terms of population size. It is inhabited by people of different cultural backgrounds from all the provinces of South Africa, and also from other countries.

5.2 The population of Gauteng

Census 2011 results indicate that Gauteng’s three metropolitan areas (City of Johannesburg, City of Tshwane and Ekurhuleni) contained the bulk of Gauteng residents, with the City of Johannesburg being occupied by approximately 36,1% of the Gauteng population on the census night (9/10 October 2011). West Rand district municipality recorded the smallest percentage of the population at 6,7%.

Approximately 78,0% of the population was black African, followed by the white population at 16,0%, coloured population at 4,0% and Indian/Asian at 3,0%. This profile is similar to that recorded in Census 2001. The most frequently spoken home language in 2011 was isiZulu (19,8%), followed by Afrikaans (12,4%) and Sesotho (11,6%). The least spoken official first language was Sign language (0,4%), followed by Siswati (1,1%), and Tshivenda (2,3%). Other non-official languages were also spoken.

5.3 Migration

According to Census 2011, the largest number of internal migrants into Gauteng from other provinces was from Limpopo, followed by KwaZulu-Natal, Mpumalanga and Eastern Cape.

5.4 Education

Census 2011 results show that substantial progress has been made as far as access to education opportunities is concerned. The proportion of persons aged 5–24 years who are attending school has increased since 2001. The proportion of persons aged 20 years and older who have completed higher education has also increased.

5.5 The labour market

Census 2011 indicates that the unemployment rate among the black African population group is highest, while among the white population group it is the lowest. The unemployment rates patterns between males and females are similar, but the unemployment rates among women are higher than those among men.

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5.6 Households and household services

The total households in Gauteng have increased from 2,069 million in 1996 to 2,791 million in 2001, and to 3,909 million in 2011.

The proportion of households living in formal dwellings increased from 74,9% in 1996 to 79,8% in 2011. There was a significant decrease in the proportion of households living in informal dwellings, from 24,1% in 1996 to 23,9% in 2001, and to 18,9% in 2011. Regarding household services and access to facilities, the findings indicate that the households that own and have fully paid off their dwellings in 2011 had decreased from 31,8% in 2001 to 27,9% in 2011. The proportion of households whose refuse was removed by the local authority at least weekly increased from 80,9% in 1996 to 88,3% in 2011. Regarding toilet facilities, there is an increase in the proportion of households with access to flush or chemical toilets from 81,5% in 1996 to 86,5% in 2011. The profile indicates that there has been an increase in the proportion of households which use piped water, from 82,7% in 2001 to 89,4% in 2011. Electricity usage has increased since 1996 to 87,4% for lighting, 74,7% for heating and 83,9% for cooking.

Regarding household goods and facilities, the findings indicate that there has been an increase in the proportion of households owning a television, computer, refrigerator and cellphones in working order between 2001 and 2011.

The above findings indicate that significant progress has been made with regard to provision of basic services in spite of the high increase in population size over the 1996–2011 period. These are positive outcomes in terms of household access to services and facilities, and are likely to be among the factors that contributed to the improvement in the living conditions of households or poverty in Gauteng.

Statistics South Africa

Census 2011 Provincial Profile: Gauteng, Report 03-01-76

43

Annexure Table 1: Percentage distribution of total population by five-year age groups and sex, Gauteng, Census 2011

Age groups Male Female 0–4 4,9 4,8 5–9 3,7 3,7 10–14 3,3 3,3 15–19 3,7 3,8 20–24 5,7 5,5 25–29 6,3 5,8 30–34 5,3 4,6 35–39 4,4 3,9 40–44 3,4 3,2 45–49 2,7 2,8 50–54 2,2 2,4 55–59 1,7 1,9 60–64 1,2 1,3 65–69 0,7 0,9 70–74 0,5 0,7 75–79 0,3 0,5 80–84 0,2 0,3 85+ 0,1 0,2

Figure 1: Distribution of total population by five-year age groups and sex, South Africa, Census 2011

5,5

4,7

4,5

4,8

5,2

4,9

3,9

3,3

2,7

2,3

2,0

1,6

1,2

0,8

0,6

0,3

0,2

0,1

5,4

4,6

4,3

4,8

5,2

4,9

3,8

3,4

3,0

2,8

2,3

1,9

1,5

1,1

0,9

0,6

0,4

0,3

6,0 4,0 2,0 0,0 2,0 4,0 6,0 8,0

0 - 45 - 9

10 - 1415 - 1920 - 2425 - 2930 - 3435 - 3940 - 4445 - 4950 - 5455 - 5960 - 6465 - 6970 - 7475 - 7980 - 84

85+

Female_SA

Male_SA

Statistics South Africa

Census 2011 Provincial Profile: Gauteng, Report 03-01-76

44

Figure 2: Distribution of total population by five-year age groups and sex, Johannesburg, Census 2011

Figure 3: Distribution of total population by five-year age groups and sex, Ekurhuleni, Census 2011

4,9

3,6

3,13,5

5,7

6,7

5,7

4,5

3,4

2,6

2,1

1,6

1,1

0,7

0,5

0,3

0,20,1

4,8

3,6

3,13,6

5,6

6,2

5,0

4,0

3,2

2,7

2,3

1,8

1,3

0,8

0,6

0,4

0,30,2

8,0 6,0 4,0 2,0 0,0 2,0 4,0 6,0 8,0

0 - 4

5 - 9

10 - 14

15 - 19

20 - 24

25 - 2930 - 34

35 - 39

40 - 44

45 - 49

50 - 54

55 - 59

60 - 64

65 - 69

70 - 74

75 - 79

80 - 84

85+

Female

Male

5,03,8

3,53,7

5,66,5

5,64,5

3,52,8

2,21,7

1,20,7

0,50,20,10,1

5,03,7

3,43,8

5,35,7

4,53,8

3,22,8

2,31,8

1,30,90,60,40,30,2

8,0 6,0 4,0 2,0 0,0 2,0 4,0 6,0 8,0

0 - 45 - 9

10 - 1415 - 1920 - 2425 - 2930 - 3435 - 3940 - 4445 - 4950 - 5455 - 5960 - 6465 - 6970 - 7475 - 7980 - 84

85+

Female

Male

Statistics South Africa

Census 2011 Provincial Profile: Gauteng, Report 03-01-76

45

Figure 4: Distribution of total population by five-year age groups and sex, City of Tshwane, Census 2011

Figure 5: Distribution of total population by five-year age groups and sex, Sedibeng, Census 2011

4,73,6

3,33,8

5,96,0

5,04,2

3,42,7

2,21,8

1,20,8

0,50,3

0,20,1

4,73,6

3,24,0

5,85,6

4,53,9

3,42,9

2,41,9

1,41,00,70,50,40,3

8,0 6,0 4,0 2,0 0,0 2,0 4,0 6,0 8,0

0 - 45 - 9

10 - 1415 - 1920 - 2425 - 2930 - 3435 - 3940 - 4445 - 4950 - 5455 - 5960 - 6465 - 6970 - 7475 - 7980 - 84

85+

Female

Male

4,94,0

3,84,3

5,55,2

4,43,8

3,22,7

2,42,0

1,40,9

0,60,3

0,20,1

4,94,0

3,74,4

5,24,7

4,03,6

3,23,0

2,62,2

1,61,1

0,80,5

0,30,2

6,0 4,0 2,0 0,0 2,0 4,0 6,0

0 - 45 - 9

10 - 1415 - 1920 - 2425 - 2930 - 3435 - 3940 - 4445 - 4950 - 5455 - 5960 - 6465 - 6970 - 7475 - 7980 - 84

85+

Female

Male

Statistics South Africa

Census 2011 Provincial Profile: Gauteng, Report 03-01-76

46

Figure 6: Distribution of total population by five-year age groups and sex, West Rand, Census 2011

4,83,8

3,53,8

5,76,0

5,14,4

3,83,5

2,92,0

1,20,7

0,50,3

0,10,1

4,73,8

3,53,8

4,95,1

4,33,7

3,33,0

2,41,8

1,20,80,6

0,40,30,2

6,0 4,0 2,0 0,0 2,0 4,0 6,0 8,0

0 - 45 - 9

10 - 1415 - 1920 - 2425 - 2930 - 3435 - 3940 - 4445 - 4950 - 5455 - 5960 - 6465 - 6970 - 7475 - 7980 - 84

85+

Female

Male

Stat

istic

s Sou

th A

fric

a

Cen

sus 2

011

Prov

inci

al P

rofil

e: G

aute

ng, R

epor

t 03-

01-7

6

47

Tabl

e 2:

Pop

ulat

ion

dist

ribut

ion

by o

ffici

al la

ngua

ge m

ost o

ften

spok

en a

t hom

e w

ithin

eac

h di

stric

t mun

icip

ality

, Cen

sus 2

011

Dist

rict a

nd

loca

l mun

icip

aliti

es

Lang

uage

s

Afrik

aans

En

glis

h Is

iNde

bele

Is

iXho

sa

IsiZ

ulu

Sepe

di

Seso

tho

Sets

wan

a Si

gnla

ngua

ge

Sisw

ati

Tshi

vend

a Xi

tson

ga

Sedi

beng

13

6 98

9 49

371

6

639

64 6

08

144

231

14 4

39

422

199

20 7

84

8 90

1 3

861

4 35

6 12

060

Em

fule

ni

89 4

48

31 5

30

3 32

1 55

935

93

858

11

382

37

5 03

0 18

144

7

671

3 02

7 3

435

9 49

8 M

idva

al

28 7

55

13 0

11

894

4 96

5 10

989

1

746

26 0

01

1 53

9 58

8 38

4 50

1 1

587

Lese

di

18 7

89

4 83

0 2

421

3 70

8 39

384

1

311

21 1

68

1 10

1 64

2 45

0 42

3 98

1 W

est R

and

135

687

53 3

22

8 54

1 11

9 50

8 72

447

24

624

86

547

21

8 99

4 4

071

7 33

2 11

577

41

847

M

ogal

e Ci

ty

60 9

15

34 4

55

5 67

3 30

963

39

876

13

956

19

776

11

2 38

9 1

725

1 84

8 8

346

15 6

60

Rand

font

ein

42 5

43

7 70

7 1

191

11 8

17

9 17

7 5

967

9 46

2 50

130

50

4 63

6 1

131

4 89

0 W

esto

naria

8

091

3 86

4 63

0 29

955

11

466

1

914

20 9

55

16 1

43

735

2 08

5 1

110

11 6

19

Mer

afon

g Ci

ty

24 1

35

7 29

6 1

047

46 7

73

11 9

28

2 78

4 36

354

40

329

1

107

2 76

6 98

7 9

678

Ekur

hule

ni

375

612

377

934

75 1

50

252

756

908

001

359

247

315

807

90 3

06

12 4

65

44 9

67

48 2

25

208

866

City

of J

ohan

nesb

urg

318

063

878

229

126

585

298

524

1 02

2 74

5 31

7 27

7 42

0 11

7 33

5 71

2 18

792

35

928

14

1 43

5 28

7 62

5 Ci

ty o

f Tsh

wan

e

536

589

244

608

163

578

61 4

46

242

610

567

312

150

420

428

799

8 51

7 44

463

66

528

24

6 11

4 Ga

uten

g 1

502

940

1 60

3 46

4 38

0 49

3 79

6 83

9 2

390

034

1 28

2 89

6 1

395

090

1 09

4 59

8 52

743

13

6 55

1 27

2 12

1 79

6 51

2

Statistics South Africa

Census 2011 Provincial Profile: Gauteng, Report 03-01-76

48

Table 3: Persons aged 5–24 years currently attending an educational institution by type of institution, Census 2011

District and local municipalities

Public Private Male Female Male Female

Sedibeng 91 780 90 272 11 232 11 855 Emfuleni 74 350 73 711 9 205 9 936 Midvaal 7 625 7 171 1 235 1 178 Lesedi 9 806 9 390 792 741 West Rand 69 225 68 716 9 493 9 639 Mogale City 30 147 29 853 4 819 4 878 Randfontein 14 099 13 891 1 378 1 466 Westonaria 8 717 8 854 1 043 1 075 Merafong City 16 261 16 117 2 252 2 220 Ekurhuleni 264 487 258 258 42 650 44 687 City of Johannesburg 312 962 311 192 83 869 87 238 City of Tshwane 253 477 252 634 45 453 48 143 Gauteng 991 931 981 072 192 697 201 562

Stat

istic

s Sou

th A

fric

a

Cen

sus 2

011

Prov

inci

al P

rofil

e: G

aute

ng, R

epor

t 03-

01-7

6

49

Tabl

e 4:

Hig

hest

leve

l of e

duca

tion

atta

ined

by

pers

ons a

ged

20 y

ears

and

old

er, C

ensu

s 201

1

Dist

rict a

nd

loca

l mun

icip

aliti

es

No

scho

olin

g So

me

prim

ary

Com

plet

ed p

rimar

y So

me

seco

ndar

y Gr

ade

12/S

td 1

0 Hi

gher

M

ale

Fem

ale

Mal

e Fe

mal

e M

ale

Fem

ale

Mal

e Fe

mal

e M

ale

Fem

ale

Mal

e Fe

mal

e Se

dibe

ng

12 1

46

14 4

26

27 9

92

31 5

37

10 3

28

10 8

86

108

143

109

052

94 2

36

95 8

82

38 9

14

38 3

93

Emfu

leni

8

299

10 3

21

21 1

87

24 8

89

7 69

0 8

453

84 8

24

87 6

33

74 7

11

76 9

46

30 0

27

30 4

92

Mid

vaal

1

718

1 62

8 3

047

2 73

6 1

313

1 10

0 11

684

10

315

10

486

10

212

5

204

4 59

6 Le

sedi

2

129

2 47

6 3

758

3 91

1 1

324

1 33

3 11

634

11

104

9

039

8 72

5 3

683

3305

W

est R

and

15 3

13

12 8

98

34 2

58

26 9

93

14 6

57

11 9

28

105

723

96 5

34

83 3

71

80 1

35

29 4

31

29 3

76

Mog

ale

City

5

993

5 38

1 11

959

10

566

5

099

4 67

4 43

365

41

117

39

362

39

521

16

996

17

266

Ra

ndfo

ntei

n 2

058

1 89

9 4

898

4 69

0 2

113

1 88

9 18

695

18

833

15

678

15

968

5

717

5 83

9 W

esto

naria

2

702

1 96

3 7

277

4 26

3 3

024

2 10

1 17

028

13

243

10

958

8

850

2 10

3 1

905

Mer

afon

g Ci

ty

4 55

9 3

655

10 1

23

7 47

4 4

420

3 26

4 26

635

23

341

17

373

15

796

4

614

4 36

7 Ek

urhu

leni

37

047

40

012

84

214

76

350

37

306

33

805

39

1 49

2 36

2 82

0 39

1 45

1 36

7 22

4 15

7 88

2 15

4 57

9 Ci

ty o

f Joh

anne

s-bu

rg

43 7

47

44 4

51

102

953

97 1

23

53 0

88

49 9

31

499

318

489

356

528

151

526

194

285

433

299

544

City

of T

shw

ane

35 5

31

45 7

40

65 4

61

66 1

09

27 7

93

27 8

08

282

388

270

124

326

409

339

396

222

802

235

968

Gaut

eng

143

783

157

527

314

879

298

112

143

171

134

358

1 38

7 06

4 1

327

887

1 42

3 61

8 1

408

830

734

461

757

861

Statistics South Africa

Census 2011 Provincial Profile: Gauteng, Report 03-01-76

50

Table 5: Percentage distribution of population aged 5 years and older by degree of difficulty (seeing)

District and local municipalities

Seeing No difficulty Some difficulty A lot of difficulty Cannot do at all

Male Female Male Female Male Female Male Female Sedibeng 88,2 84,0 10,0 13,4 1,6 2,5 0,1 0,1 Emfuleni 88,0 83,5 10,2 13,8 1,7 2,6 0,1 0,1 Midvaal 89,1 86,5 9,4 11,4 1,4 2,0 0,1 0,1 Lesedi 89,1 84,9 9,4 12,7 1,4 2,2 0,1 0,1 West Rand 89,1 85,1 9,6 12,9 1,2 2,0 0,1 0,1 Mogale City 89,8 86,0 9,0 12,2 1,1 1,7 0,1 0,1 Randfontein 88,9 84,6 9,8 13,5 1,2 1,8 0,1 0,1 Westonaria 89,4 85,4 9,2 12,0 1,3 2,5 0,1 0,1 Merafong City 87,8 83,4 10,6 14,1 1,4 2,4 0,1 0,2 Ekurhuleni 91,2 87,4 7,6 10,9 1,0 1,6 0,1 0,1 City of Johannesburg 92,5 88,9 6,7 9,8 0,7 1,2 0,1 0,1 City of Tshwane 91,7 88,8 7,4 9,9 0,8 1,2 0,1 0,1 Gauteng 91,4 87,9 7,5 10,5 0,9 1,5 0,1 0,1

Table 6: Percentage distribution of population aged 5 years and older by degree of difficulty (hearing)

District and local municipalities

Hearing No difficulty Some difficulty A lot of difficulty Cannot do at all Male Female Male Female Male Female Male Female

Sedibeng 96,4 96,1 3,0 3,2 0,5 0,6 0,1 0,1 Emfuleni 96,6 96,2 2,8 3,2 0,5 0,6 0,1 0,1 Midvaal 95,5 95,7 3,7 3,5 0,7 0,7 0,1 0,1 Lesedi 96,3 95,9 3,1 3,4 0,5 0,6 0,1 0,1 West Rand 96,2 96,2 3,2 3,2 0,5 0,5 0,1 0,1 Mogale City 96,8 96,6 2,7 2,9 0,4 0,4 0,1 0,1 Randfontein 96,3 96,3 3,1 3,1 0,5 0,5 0,1 0,1 Westonaria 96,1 96,2 3,3 3,1 0,5 0,6 0,1 0,1 Merafong City 95,0 95,5 4,2 3,8 0,6 0,6 0,1 0,1 Ekurhuleni 97,4 97,1 2,2 2,4 0,3 0,4 0,1 0,1 City of Johannesburg 97,9 97,5 1,8 2,1 0,2 0,3 0,1 0,1 City of Tshwane 97,4 97,2 2,2 2,4 0,3 0,4 0,1 0,1 Gauteng 97,4 97,1 2,2 2,4 0,3 0,4 0,1 0,1

Statistics South Africa

Census 2011 Provincial Profile: Gauteng, Report 03-01-76

51

Table 7: Percentage distribution of population aged 5 years and older by degree of difficulty (communication)

District and local municipalities

Communication No difficulty Some difficulty A lot of difficulty Cannot do at all Male Female Male Female Male Female Male Female

Sedibeng 98,7 98,8 0,9 0,9 0,2 0,2 0,1 0,1 Emfuleni 98,8 98,9 0,9 0,8 0,2 0,2 0,1 0,1 Midvaal 98,4 98,6 1,3 1,1 0,1 0,2 0,1 0,1 Lesedi 98,6 98,8 1,0 0,9 0,3 0,2 0,1 0,1 West Rand 98,7 98,7 1,0 1,0 0,2 0,2 0,1 0,1 Mogale City 98,8 98,8 0,9 0,9 0,2 0,2 0,1 0,1 Randfontein 98,7 98,8 0,9 0,9 0,2 0,2 0,2 0,1 Westonaria 98,4 98,5 1,3 1,2 0,2 0,2 0,2 0,1 Merafong City 98,7 98,5 1,0 1,1 0,2 0,2 0,1 0,1 Ekurhuleni 98,9 98,9 0,8 0,8 0,2 0,2 0,1 0,1 City of Johannesburg 99,0 99,0 0,7 0,8 0,2 0,2 0,1 0,1 City of Tshwane 98,8 98,9 0,9 0,9 0,2 0,2 0,1 0,1 Gauteng 98,9 98,9 0,8 0,8 0,2 0,2 0,1 0,1

Table 8: Percentage distribution of population aged 5 years and older by degree of difficulty (walking or climbing stairs)

District and local municipalities

Walking or climbing stairs No difficulty Some difficulty A lot of difficulty Cannot do at all Male Female Male Female Male Female Male Female

Sedibeng 97,2 96,2 2,0 2,8 0,6 0,9 0,2 0,2 Emfuleni 97,3 96,3 1,9 2,7 0,6 0,8 0,2 0,2 Midvaal 96,5 95,5 2,5 3,2 0,7 1,0 0,2 0,3 Lesedi 97,1 96,0 2,1 2,9 0,6 0,9 0,2 0,3 West Rand 97,4 96,4 1,9 2,8 0,5 0,7 0,2 0,2 Mogale City 97,3 96,3 2,0 2,8 0,5 0,7 0,2 0,2 Randfontein 97,3 96,3 1,9 2,7 0,6 0,8 0,2 0,2 Westonaria 97,8 96,8 1,6 2,5 0,5 0,6 0,2 0,1 Merafong City 97,3 96,3 2,0 2,9 0,5 0,7 0,2 0,2 Ekurhuleni 97,9 96,9 1,6 2,4 0,4 0,6 0,2 0,2 City of Johannesburg 98,0 97,1 1,5 2,2 0,3 0,6 0,1 0,2 City of Tshwane 97,7 96,7 1,8 2,5 0,4 0,6 0,2 0,2 Gauteng 97,8 96,8 1,6 2,4 0,4 0,6 0,2 0,2

Statistics South Africa

Census 2011 Provincial Profile: Gauteng, Report 03-01-76

52

Table 9: Percentage distribution of population aged 5 years and older by degree of difficulty (remembering or concentration)

District and local municipalities

Remembering or concentration No difficulty Some difficulty A lot of difficulty Cannot do at all Male Female Male Female Male Female Male Female

Sedibeng 96,7 95,6 2,5 3,4 0,6 0,8 0,2 0,2 Emfuleni 96,7 95,5 2,5 3,4 0,6 0,9 0,2 0,2 Midvaal 96,6 96,0 2,8 3,0 0,5 0,8 0,1 0,2 Lesedi 96,8 96,0 2,5 3,1 0,5 0,7 0,2 0,2 West Rand 97,2 96,1 2,2 3,1 0,5 0,7 0,1 0,1 Mogale City 97,3 96,5 2,1 2,8 0,4 0,6 0,1 0,2 Randfontein 97,1 96,0 2,3 3,2 0,4 0,7 0,2 0,1 Westonaria 97,5 96,0 2,0 3,0 0,5 0,8 0,1 0,1 Merafong City 96,8 95,3 2,5 3,7 0,5 0,9 0,1 0,1 Ekurhuleni 97,7 96,9 1,8 2,4 0,4 0,5 0,1 0,1 City of Johannesburg 98,0 97,3 1,6 2,1 0,3 0,4 0,1 0,1 City of Tshwane 97,8 97,3 1,7 2,1 0,4 0,4 0,1 0,1 Gauteng 97,7 97,0 1,8 2,4 0,4 0,5 0,1 0,1

Table 10: Percentage distribution of population aged 5 years and older by degree of difficulty (self-care)

District and local municipalities

Self-care No difficulty Some difficulty A lot of difficulty Cannot do at all Male Female Male Female Male Female Male Female

Sedibeng 97,6 97,6 1,4 1,4 0,5 0,5 0,5 0,5 Emfuleni 97,5 97,5 1,4 1,5 0,5 0,5 0,6 0,5 Midvaal 98,2 97,9 1,3 1,5 0,2 0,4 0,3 0,3 Lesedi 98,0 97,8 1,2 1,3 0,3 0,4 0,5 0,5 West Rand 97,9 97,6 1,3 1,5 0,4 0,4 0,5 0,5 Mogale City 97,9 97,7 1,3 1,5 0,4 0,4 0,4 0,5 Randfontein 97,5 97,3 1,5 1,7 0,5 0,5 0,5 0,5 Westonaria 98,1 97,5 1,1 1,5 0,4 0,5 0,3 0,4 Merafong City 97,9 97,6 1,3 1,5 0,3 0,4 0,5 0,5 Ekurhuleni 98,2 98,0 1,1 1,3 0,3 0,3 0,4 0,4 City of Johannesburg 98,4 98,3 1,0 1,1 0,2 0,3 0,3 0,3 City of Tshwane 98,1 98,0 1,1 1,2 0,3 0,3 0,5 0,4 Gauteng 98,2 98,0 1,1 1,2 0,3 0,3 0,4 0,4

ISBN: 978-0-621-43215-2